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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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GPU cluster manager for running and managing LLMs.
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Creating semantic cache to store responses from LLM queries.
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Implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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GPT Mobile is an Android app that can chat with multiple LLMs at once! Currently supports ChatGPT, Anthropic Claude, and Google Gemini.
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A high-level machine learning library in the vein of Keras.
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Hyperparameter optimization framework, inspired by Optuna.
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"a collaborative benchmark intended to probe large language models and extrapolate their future capabilities".
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Machine learning library written in pure Go.
github.com
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
github.com
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
github.com
Package for computer vision using OpenCV 4 and beyond.
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An open source Go transpiler for machine learning models.
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Benchmarks of machine learning inference for Go.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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Fast and convenient feature processing for low latency machine learning in Go.
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Open Bilingual Pre-Trained Model, quantization of ChatGLM-130B, can run on consumer-level GPUs.
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Open Bilingual Pre-Trained Model (ICLR 2023).
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Cloud-Native LLM Routing Engine. Improve LLM app resilience and speed.
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AI code reviewer for GitHub Actions or local use, compatible with any LLM and integrated with Jira/Linear.
github.com
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
github.com
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
github.com
Testing framework dedicated to ML models, from tabular to LLMs. Detect risks of biases, performance issues and errors in 4 lines of code.